What is Product Context?
Product context is the business knowledge AI coding agents need to build the right thing: customer insights, past decisions, strategic goals, and technical constraints. Learn why it matters.
Product context is the business knowledge that AI coding agents need to build the right thing—not just syntactically correct code. It includes your customer insights, past decisions, strategic goals, technical constraints, and team velocity.
Why Product Context Matters
AI coding assistants like Cursor, Claude, and GitHub Copilot are brilliant at how to build things. But they have no idea:
- Who you're building for (your customers, their pain points)
- What you've already decided (and why you decided it)
- Where you're headed (your strategy, goals, roadmap)
- What constraints exist (technical debt, team capacity, dependencies)
Without this context, AI generates code that's technically correct but strategically wrong.
What is the product context gap?
AI coding assistants know programming languages, frameworks, and code syntax, but they don't know your ICP and customer segments, past architectural decisions, why features were rejected, team velocity and capacity, or what's already built elsewhere in your codebase.
| What AI Knows | What AI Needs to Know |
|---|---|
| Programming languages | Your ICP and customer segments |
| Frameworks and libraries | Past architectural decisions |
| Code syntax and patterns | Why features were rejected |
| Documentation (if you paste it) | Team velocity and capacity |
| Current file contents | What's already built elsewhere |
This gap is why AI coding assistants often suggest features that already exist, miss critical business requirements, or build things that don't serve your strategy.
What are the types of product context?
1. Customer Context
Who are you building for? What are their pain points? What do they care about?
Examples:
- User personas and their needs
- Customer feedback themes
- Support ticket patterns
- User research insights
2. Decision Context
What have you decided, and why? What did you reject?
Examples:
- "We chose Postgres over MongoDB because of relational data needs"
- "We rejected real-time collaboration—not core to our ICP"
- "We're using Clerk for auth to avoid building it ourselves"
3. Strategic Context
Where is the product headed? What are your goals?
Examples:
- 6-month vision and OKRs
- Competitive positioning
- Key metrics and targets
- Roadmap priorities
4. Technical Context
What constraints and patterns exist in your codebase?
Examples:
- Tech stack and frameworks
- Architectural patterns
- Technical debt areas
- Performance requirements
5. Velocity Context
How fast can your team actually ship?
Examples:
- Release frequency
- Cycle time metrics
- Team capacity
- Current work in progress
How does Brief provide product context?
Brief automatically collects and synthesizes product context from tools you already use. It extracts work pipeline and velocity from Linear or Jira, your actual built features and tech stack from GitHub, strategy and decisions from Notion or Google Docs, discussions and decisions from Slack, customer research from Fireflies or Fathom, and user behavior from PostHog.
| Tool | Context Extracted |
|---|---|
| Linear/Jira | Work pipeline, velocity, priorities |
| GitHub | What's actually built, tech stack |
| Notion/Docs | Strategy, decisions, specs |
| Slack | Discussions, decisions, context |
| Fireflies/Fathom | Customer research, feedback |
| PostHog | User behavior, feature adoption |
Brief then makes this context available to AI coding assistants via MCP (Model Context Protocol).
How is product context different from code context?
Code context is what the code does (function signatures, file contents, syntax, dependencies), while product context is why the code exists (business requirements, customer needs, strategic alignment, decision rationale).
| Code Context | Product Context |
|---|---|
| What the code does | Why the code exists |
| Function signatures | Business requirements |
| File contents | Customer needs |
| Syntax errors | Strategic alignment |
| Dependencies | Decision rationale |
Both are essential. Code context helps AI write correct code. Product context helps AI write the right code.
Real-World Example
Without Product Context:
You: "Add a feature for team collaboration" AI: "I'll add real-time collaborative editing, presence indicators, and a commenting system..."
With Product Context:
You: "Add a feature for team collaboration" AI (queries Brief): "I see we decided against real-time collaboration in Decision #42 because it's not core to our solo-founder ICP. Our personas are primarily individual users. Did you want to revisit that decision, or focus on async collaboration instead?"
Getting Started
Want to give your AI coding assistant product context?
- What is Brief? — Learn how Brief captures and provides product context
- Quick Start Guide — Set up Brief in 5 minutes
- Connect Your Tools — Start building your product context automatically